Following the release of GPT‑6 Astra earlier this month, OpenAI has introduced updated versions of the smaller Sol and Luna models. The company says these new models extend the capabilities of GPT‑6 Astra by making that intelligence more efficient and more accessible.
Model roles and target use cases
The first Sol and Luna models were introduced earlier this year as distinct tiers within OpenAI’s model lineup. Sol is positioned for more complex tasks such as coding, while Luna is intended for high‑volume, goal‑oriented clerical work like summarizing documents, extracting information, or answering quick questions.
Efficiency and pricing
OpenAI highlights significant gains in efficiency and affordability. It says the 6‑series models will be available on the API at half the cost of the 5.6‑series versions of Sol and Luna. The company attributes the price reduction largely to improvements in caching and inference.
Accuracy and error rates
The company also reports better factual accuracy and lower error rates in coding for the newest models. According to OpenAI’s announcement, “on our internal factuality evaluation, which is based on de‑identified real‑world conversations where users flagged mistakes by our models, GPT‑6 Sol makes about half as many mistakes as its predecessor, reaching Astra‑level reliability at much lower cost.”
Competitive context
OpenAI again positions its new models as superior to offerings from its main rival, Anthropic, claiming GPT‑6 Sol and Luna handle many tasks substantially better than Anthropic’s top models such as Fable and Opus. Notably, Anthropic released a new Opus 5.5 version just 90 minutes before OpenAI’s announcement, underscoring the intense competition between the companies.
Availability
The updated Sol and Luna are now available in ChatGPT Work and Codex for most paid accounts, and in the ChatGPT API. Luna will also be available in the desktop app and for Free and Go users. OpenAI expects to roll the models out to ChatGPT (app and website) gradually over the course of the day.
Why this matters
Lower‑cost, more accurate smaller models can broaden practical adoption by reducing operational costs and improving reliability for business and administrative tasks where accuracy matters. The rivalry with Anthropic appears to be accelerating development cycles and release timing for both firms.
